# How to integrate GTmetrix MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate GTmetrix MCP with OpenAI Agents SDK",
  "toolkit": "GTmetrix",
  "toolkit_slug": "gtmetrix",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/gtmetrix/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/gtmetrix/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-03-29T06:36:47.678Z"
}
```

## Introduction

This guide walks you through connecting GTmetrix to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working GTmetrix agent that can run a performance test on your homepage, check latest gtmetrix report for example.com, list top optimization recommendations for your site through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a GTmetrix account through Composio's GTmetrix MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate GTmetrix with

- [Claude Agent SDK](https://composio.dev/toolkits/gtmetrix/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/gtmetrix/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/gtmetrix/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/gtmetrix/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/gtmetrix/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/gtmetrix/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/gtmetrix/framework/cli)
- [Google ADK](https://composio.dev/toolkits/gtmetrix/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/gtmetrix/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/gtmetrix/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/gtmetrix/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/gtmetrix/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/gtmetrix/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for GTmetrix
- Configure an AI agent that can use GTmetrix as a tool
- Run a live chat session where you can ask the agent to perform GTmetrix operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## What is the GTmetrix MCP server, and what's possible with it?

The GTmetrix MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your GTmetrix account. It provides structured and secure access so your agent can perform GTmetrix operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `GTMETRIX_DELETE_PAGE` | Delete Page | Tool to delete a specific page in GTmetrix. Use when you need to permanently remove a page resource. |
| `GTMETRIX_DELETE_REPORT` | Delete Report | Tool to delete a GTmetrix report. Use when you need to remove an existing performance report from GTmetrix. |
| `GTMETRIX_GET_BROWSERS` | Get Browsers | Tool to retrieve the list of available browsers for GTmetrix performance tests. Use when you need to see which browsers are available and their testing capabilities. |
| `GTMETRIX_GET_LOCATION` | Get Location Details | Tool to retrieve location details from GTmetrix. Use when you need to get information about a specific GTmetrix test location including name, region, browser support, IP addresses, and access permissions. |
| `GTMETRIX_GET_LOCATIONS` | Get Locations | Tool to retrieve the list of available GTmetrix test locations. Use when you need to see which locations are available for testing and their details including supported browsers and access status. |
| `GTMETRIX_GET_PAGE_DETAILS` | Get Page Details | Tool to retrieve page details from the user's GTmetrix account. Use when you need to get comprehensive page information including URL, testing configuration, and monitoring frequency. |
| `GTMETRIX_GET_PAGE_REPORTS` | Get Page Reports | Tool to retrieve the report list associated with a monitored page in GTmetrix. Use when you need to access historical performance data for a specific page. Supports pagination, sorting, and filtering. |
| `GTMETRIX_GET_PAGES` | Get Pages | Tool to retrieve the page list from your GTmetrix account. Returns a paginated collection of monitored pages with their configurations and latest report information. Use when you need to view all monitored pages, check page configurations, or access latest report data. |
| `GTMETRIX_GET_REPORT` | Get Report | Tool to retrieve a GTmetrix test report by its identifier. Use when you need to get comprehensive performance metrics, timing data, and links to resources for a specific report. |
| `GTMETRIX_GET_SIMULATED_DEVICE` | Get Simulated Device | Tool to retrieve simulated device details. Use when you need information about a specific simulated device including its name, category, manufacturer, user agent, screen dimensions, and pixel ratio. |
| `GTMETRIX_GET_SIMULATED_DEVICES` | Get Simulated Devices | Tool to retrieve the list of simulated devices available in GTmetrix. Use when you need to see available device profiles for testing. |
| `GTMETRIX_GET_API_ACCOUNT_STATUS` | Get API Account Status | Tool to retrieve the current API account state and remaining credits. Use to check available API credits, refill schedule, and account features. |
| `GTMETRIX_GET_TEST_DETAILS` | Get Test Details | Tool to retrieve test details for a specific GTMetrix test. Use when you need to check the status, configuration, or results of a previously initiated test. |
| `GTMETRIX_GET_TESTS` | Get Tests | Tool to retrieve the test list from your GTmetrix account with pagination and filtering support. Use when you need to view tests with their state, timestamps, and configuration details. |
| `GTMETRIX_RETEST_REPORT` | Retest Report | Tool to initiate a retest of a completed GTmetrix report with same parameters. Use when you need to rerun a test using the exact same analysis parameters as the original test. |
| `GTMETRIX_START_TEST` | Start Test | Tool to start a new GTmetrix test for a specified URL. Use when you need to analyze website performance with configurable options like location, browser, and throttling. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The GTmetrix MCP server is an implementation of the Model Context Protocol that connects your AI agent to GTmetrix. It provides structured and secure access so your agent can perform GTmetrix operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live GTmetrix project
- Some knowledge of Python or Typescript

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like GTmetrix.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only gtmetrix.
- The router checks the user's GTmetrix connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access GTmetrix.
- This approach keeps things lightweight and lets the agent request GTmetrix tools only when needed during the conversation.
```python
# Create a GTmetrix Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["gtmetrix"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for GTmetrix
const session = await composio.create(userId as string, {
  toolkits: ['gtmetrix'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access GTmetrix. "
        "Help users perform GTmetrix operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access GTmetrix. Help users perform GTmetrix operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["gtmetrix"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access GTmetrix. "
        "Help users perform GTmetrix operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['gtmetrix'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access GTmetrix. Help users perform GTmetrix operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating GTmetrix MCP with OpenAI Agents SDK to build a functional AI agent that can interact with GTmetrix.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build GTmetrix MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/gtmetrix/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/gtmetrix/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/gtmetrix/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/gtmetrix/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/gtmetrix/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/gtmetrix/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/gtmetrix/framework/cli)
- [Google ADK](https://composio.dev/toolkits/gtmetrix/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/gtmetrix/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/gtmetrix/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/gtmetrix/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/gtmetrix/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/gtmetrix/framework/crew-ai)

## Related Toolkits

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- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
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- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.
- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and GTmetrix MCP?

With a standalone GTmetrix MCP server, the agents and LLMs can only access a fixed set of GTmetrix tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from GTmetrix and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right GTmetrix tools.

### Can I manage the permissions and scopes for GTmetrix while using Tool Router?

Yes, absolutely. You can configure which GTmetrix scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your GTmetrix data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)